Accurate　prediction　on　influent　wastewater　quality　is　of　great　importance　to　energy　saving　and　chemical　dosage　reduction　of　wastewater　treatment　plants　(WWTPs).　However,　the　existing　methods　ignore　the　data　noise　caused　by　water　sensors　working　in　harsh　conditions　and　the　intrinsic　variable　dynamics　inherent　in　the　time　series　of　wastewater　quality.　To　tackle　this　problem,　we　propose　a　novel　approach　called　wt-ResLSTM　(wavelet　transform　and　Residual　Long　Short　-Term　Memory)　to　predict　the　influent　wastewater　quality.　Specifically,　we　adopt　wavelet　transform　and　semi-soft　thresholding　to　remove　the　noise　from　influent　wastewater　quality　data　adaptively.　Then,　we　use　autoencoder　to　learn　the　latent　representation　of　the　recent　fluctuation　of　wastewater　quality　to　capture　its　transient　uncertainty.　Further,　the　residual　LSTM　is　adopted　to　learn　both　the　long-term　and　short-term　sequential　dependencies　of　influent　wastewater　quality　from　the　historical　wastewater　quality　and　the　latent　representation　of　its　recent　fluctuation.　Experiments　on　the　dataset　from　a　large-scale　urban　WWTP　in　Beijing　demonstrate　that　the　proposed　wt-ResLSTM　approach　outperforms　state-of-the-art　techniques　in　predicting　influent　wastewater　quality　in　terms　of　level　accuracy　and　directional　accuracy.